Time-Bounded Mission Planning in Time-Varying Domains with Semi-MDPs and Gaussian Processes
Time-Bounded Mission Planning in Time-Varying Domains with Semi-MDPs and Gaussian Processes
复制标题
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Paul Duckworth;Bruno Lacerda;Nick Hawes
中科院分区：
文献类型：
--
作者：
Paul Duckworth;Bruno Lacerda;Nick Hawes
：不确定的、时变的动态环境在真实的机器人世界中无处不在。我们提出了一个在线规划框架，以解决时变动态下的有时限的任务，这些动态影响的持续时间和结果的行动。我们提出这样的问题，半马尔可夫决策过程中，行动的持续时间分布根据一个先验未知的时变函数。我们的方法保持了对这个函数的信念，时间通过一个离散的搜索树传播，有效地维护了一个可达状态的子集。我们表现出改进的使命性能的海洋车辆模拟器根据现实世界的时空洋流，并展示了解决共同安全的线性时序逻辑问题，这是更复杂的可达性问题，在以前的方法解决的能力。
: Uncertain, time-varying dynamic environments are ubiquitous in real world robotics. We propose an online planning framework to address time-bounded missions under time-varying dynamics, where those dynamics affect the duration and outcome of actions. We pose such problems as semi-Markov decision processes, where actions have a duration distributed according to an a priori unknown time-varying function. Our approach maintains a belief over this function, and time is propagated through a discrete search tree that efficiently maintains a subset of reachable states. We show improved mission performance on a marine vehicle simulator acting under real-world spatio-temporal ocean currents, and demonstrate the ability to solve co-safe linear temporal logic problems, which are more complex than the reachability problems tackled in previous approaches.